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Project Idea
Kubernetes Cost Optimizer and Rightsizer
A tool that analyzes Kubernetes resource usage and recommends rightsizing changes to reduce cloud spending — with confidence scoring and safety margins.
Kubernetes Cost Optimizer and Rightsizer
A tool that continuously monitors Kubernetes resource requests and limits, compares them against actual usage, and recommends or automatically applies rightsizing changes — helping teams reduce cloud spending without sacrificing reliability.
Who Is This For?
- DevOps and SRE teams managing Kubernetes clusters who need to reduce cloud costs
- Platform engineers responsible for cluster efficiency and resource allocation
- CTOs and engineering leaders at startups watching their cloud bills grow
- Cloud cost optimization teams looking for Kubernetes-specific tooling
The Problem
Kubernetes teams typically over-provision resources because they’re afraid of outages. When an engineer sets CPU and memory requests, they estimate high “just to be safe.” The result: clusters run at 20-40% utilization while the company pays for 100% of the provisioned resources.
Industry surveys suggest many Kubernetes clusters run at 30-50% resource utilization, meaning a significant portion of provisioned capacity goes unused. For a team spending $50,000/month on cloud infrastructure, even a 20% reduction in over-provisioning could save $10,000/month.
Existing tools like Kubecost and OpenCost show you what you’re spending but don’t tell you what to do about it. They’re dashboards, not optimizers. The actual rightsizing — analyzing usage patterns, recommending changes, and applying them — is still manual work that requires deep Kubernetes expertise.
The gap: teams know they’re overspending but don’t have the tools to systematically identify and fix resource waste without risking outages.
How It Works
The tool works in three phases:
Rightsizing Logic
For each container: actual_cpu_p95 = 95th percentile CPU usage over observation window actual_mem_p95 = 95th percentile memory usage over observation window recommended_cpu = actual_cpu_p95 × safety_margin (1.2-1.5x) recommended_mem = actual_mem_p95 × safety_margin (1.3-1.5x) confidence = HIGH if observation window ≥ 14 days MEDIUM if 7-14 days LOW if < 7 days Cost Savings Calculator
current_cost = Σ (cpu_request × cpu_price + memory_request × mem_price) optimized_cost = Σ (recommended_cpu × cpu_price + recommended_mem × mem_price) savings = current_cost - optimized_cost savings_percentage = (savings / current_cost) × 100 Core Workflow
Kubernetes Cluster → Metrics Collection → Usage Analysis → Rightsizing Engine → Recommendations ↓ Apply / Export / Report Key Features
- Multi-cluster support — Monitor and optimize across multiple Kubernetes clusters
- Prometheus/Datadog integration — Pull metrics from existing monitoring stack
- Confidence scoring — Every recommendation includes a confidence level based on data maturity
- Safety margins — Configurable safety multipliers (default 1.2x for CPU, 1.3x for memory)
- Dry-run mode — Preview changes without applying them
- Auto-apply with guardrails — Automatically apply HIGH-confidence recommendations with rollback capability
- Cost dashboard — Real-time view of current spending, potential savings, and realized savings
- Slack/Teams alerts — Notify when new recommendations are available or when costs spike
- Historical tracking — Track rightsizing changes and their impact on costs and reliability
Technical Architecture
┌─────────────────────────────────────────────┐ │ Kubernetes Clusters │ │ (Metrics Server / Prometheus / Datadog) │ └──────────────────┬──────────────────────────┘ │ ┌─────────▼─────────┐ │ Metrics Collector │ │ (Prometheus Query │ │ / Datadog API) │ └─────────┬─────────┘ │ ┌─────────▼─────────┐ │ Usage Analyzer │ │ (Profiling + │ │ Statistical) │ └─────────┬─────────┘ │ ┌─────────▼─────────┐ │ Rightsizing Engine│ │ (Recommendations │ │ + Cost Calc) │ └─────────┬─────────┘ │ ┌──────────────┼──────────────┐ │ │ │ ┌───▼───┐ ┌────▼────┐ ┌────▼────┐ │Web │ │K8s API │ │Slack / │ │Dash- │ │(Apply │ │Alerts │ │ board │ │ changes)│ │ │ └───────┘ └─────────┘ └─────────┘ Technology Choices
| Component | Technology | Why | |||
| Metrics | Prometheus (PromQL) | Standard K8s monitoring | ||||
| K8s client | kubernetes Python client | Native cluster interaction | ||||
| Database | PostgreSQL | Recommendation history, cost tracking | ||||
| Frontend | React | Dashboard and visualization | ||||
| Alerts | Slack API, Microsoft Teams | Team notifications | ||||
| Deployment | Helm chart | Easy K8s-native deployment | ||||
| Auth | OIDC / Kubernetes RBAC | Cluster-native authentication | ||||
| Tool | Focus | Limitation |
| OpenCost | Cost allocation | Metrics, not recommendations | |
| CAST AI | Auto-optimization | SaaS, expensive, black-box | |
| Spot.io | Spot instance optimization | Focuses on instance types, not container rightsizing | |
